LearnDataSci is on a mission to democratize data science and machine learning knowledge online, and we’d love for you to help us in our mission by publishing incredible educational content
If you don’t have anything written but would really like to contribute here’s some common sources of inspiration:
Recent academic papers
In an intuitive way describe how something was done and replicate the code to achieve the same result. Distill important research into a more compact and easier to digest format.
A project you’ve worked on
Whether it was a hobby or for work, past projects are a great source of interesting articles. It will also force you to revisit, refactor, and organize your old code for presentation.
Something that took you forever to figure out
Did something finally click? Write down how and why you had a revelation in data science and help spark that understanding in others.
Data science topics
There’s an incredible amount of information beginner’s need to learn to get into data science, and it’s not all about machine learning. Some inspiration:
Anything else that could be beneficial to someone that will start searching for a job would also be helpful. What do you wish you would have known? What article do you wish was written when you were starting out? Write about that!
Proposed articles should be unique and unpublished elsewhere. Although there’s no restrictions on reposting your article after we publish, we do ask you wait just one week until cross-posting to other outlets.
All articles should be written in Jupyter notebooks using proper Markdown and posted to Google Colab for sharing with our editors and students. All math should be presented using LaTeX in Markdown cells of the notebook. Assets, like images, videos, GIFs, or helper Python scripts, should be included with the notebook
Python and PEP 8
Currently we’re only accepting Python articles. Please follow the PEP 8 standards for Python. Our articles are sometimes the first point of contact for new programmers so our Python needs to be presented in a clean, organized, and and presented in a standardized way.
Articles that use illegally collected data, or data that’s normally behind a user account or paywall, will be turned away. Please include any data you’re using with the article submission.
Try finding or creating, images, videos, and other resources to help readers learn more effectively. Many readers click away when faced with giant walls of text. Media can help explain certain concepts much better than words alone. If using media that is not your own, please attribute the creator by providing a link to the source.